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Churn Analysis

User churn analysis is the same as churn analytics. Customer churn analysis is the process of analyzing why users leave the business – be is stop using the product, service, subscription etc. Reducing churn by 5% can increase profits by 25-125%. Business must analyze why customer churn is happening and make it stop. Bad customer experience is the No.1 reason most customers leave; 68% of them leave because they think a company doesn’t care about them.

The Right KPIs To Track

The right KPIs to inform about Churn must be identified to let the business know their churn rate and determine whether it’s tolerable or not.

i. Gross customer churn rate:  

The rate at which customers leave the business daily, monthly, quarterly or yearly.

          No. of churned customers in a period
          __________________________________________          X100

          No. of customers at the start of that period

Churn can also be calculated on a cohort basis, that is, calculating churn based on a group of customers. For example, how many product trials in January left before February, March or some predetermined timeline? A tolerable churn rate is generally <12.30%, but an acceptable value is around 5%.

 ii. Net customer churn rate:

The difference between the rate of customer acquisitions vs the rate of cancellations.

         {% of customer acquisitions – % of customer cancellations}

A negative result means there are more activations than deactivations (cancellations). If the result is positive, the business has more cancellations than customer acquisition — which is the ‘unhealthy’ churn business must look out for.

iii. Daily average usage (DAU):

The rate at which users engage with the business product, website or software. It’s a great indicator of customer engagement. If DAU is getting lower and lower for seven days, then there’s probably a glitch or bad customer experience the business needs to fix to stop such an unhealthy churn rate.

iv. Weekly and monthly average usage (WAU and MAU):

The rate at which users interact with the product every week (WAU) or every month (MAU). Like DAU, an ascending WAU or MAU reflects a good customer experience, and a descending weekly/monthly usage means the business must address the decreasing customer experience, well before the churn rate gets too bad. 

In Conclusion

Businesses need to understand the factors that could cause the average usage rates to drop — and a qualitative analysis tool is required for that. These tools will typically show how and why customers are taking specific actions. A quantitative analysis tool like ZOHO Analytics will show the who, why, where, and how many things happening on your software, and qualitative analytics tools show you how and why those things happen.